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Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation

arXiv

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arXiv 2023年

作者： Díaz-Rodríguez, Natalia Ser, Javier Del Coeckelbergh, Mark de Prado, Marcos López Herrera-Viedma, Enrique Herrera, Francisco Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain Derio48160 Spain Bilbao48013 Spain Department of Philosophy University of Vienna Vienna1010 Austria School of Engineering Cornell University IthacaNY14850 United States ADIA Lab Al Maryah Island Abu Dhabi United Arab Emirates Department of Mathematics Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

MSC Codes 68T01Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system’s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight;robustness and safety;privacy and data governance;transparency;diversity, non-discrimination and fairness;societal and environmental wellbeing;and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society. © 2023, CC BY-NC-ND.

关键词： Artificial intelligence

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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science China(Technological sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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Correction: Unbiased and multilevel methods for a class of diffusions partially observed via marked point processes

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Statistics and Computing 2024年第6期34卷 1-1页

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

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An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

SSRN

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SSRN 2022年

作者： Sun, Qi Wu, Jian Chiclana, Francisco Wang, Sha Herrera-Viedma, Enrique School of Economics and Management Shanghai Maritime University Shanghai201306 China Center for Artificial Intelligence and Decision Sciences Shanghai Maritime University Shanghai201306 China Institute of Artificial Intelligence Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

This paper proposes a theoretical framework to prevent weight manipulation in large-scale group decision making (LSGDM). Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the rights index (RI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’manipulation behavior. The minimum adjustment rule aims for ‘effificiency’ while the maximum entropy rule aims for ‘justice’. Consequently, by keeping a balance between ‘effificiency’ and ‘justice’ in the mechanism of assigning weights, the rationality of weight allocation to reach consensus effificiently can be ensured. Detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Finite-size effects on the convergence time in continuous-opinion dynamics

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Physical Review E 2021年第1期104卷 014309-014309页

作者： Hang-Hyun Jo Naoki Masuda Department of Physics The Catholic University of Korea Bucheon 14662 Republic of Korea Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

We study finite-size effects on the convergence time in a continuous-opinion dynamics model. In the model, each individual's opinion is represented by a real number on a finite interval, e.g., [0,1], and a uniformly randomly chosen individual updates its opinion by partially mimicking the opinion of a uniformly randomly chosen neighbor. We numerically find that the characteristic time to the convergence increases as the system size increases according to a particular functional form in the case of lattice networks. In contrast, unless the individuals perfectly copy the opinion of their neighbors in each opinion updating, the convergence time is approximately independent of the system size in the case of regular random graphs, uncorrelated scale-free networks, and complete graphs. We also provide a mean-field analysis of the model to understand the case of the complete graph.

关键词： Complex systems Nonequilibrium statistical mechanics Social systems Homogeneous networks Scale free & inhomogeneous networks Social dynamics Master equation Monte Carlo methods Network Models

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Label-Efficient Self-Supervised Federated Learning for Tackling data Heterogeneity in Medical Imaging

arXiv

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arXiv 2022年

作者： Yan, Rui Qu, Liangqiong Wei, Qingyue Huang, Shih-Cheng Shen, Liyue Rubin, Daniel Xing, Lei Zhou, Yuyin The Institute for Computational and Mathematical Engineering Stanford University StanfordCA94305 United States The Department of Statistics and Actuarial Science The Institute of Data Science The University of Hong Kong 999077 Hong Kong The Department of Biomedical Data Science Stanford University StanfordCA94305 United States The Department of Electrical Engineering Stanford University StanfordCA94305 United States The Department of Biomedical Informatics Stanford University StanfordCA94305 United States The Department of Radiation Oncology Stanford University StanfordCA94305 United States The Department of Computer Science and Engineering University of California Santa CruzCA95064 United States

The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://***/rui-yan/SSL-FL. © 2022, CC BY.

关键词： Deep learning

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ROPDet: real-time anchor-free detector based on point set representation for rotating object

ROPDet: real-time anchor-free detector based on point set re...

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作者： Yang, Zhixiang He, Kunkun Zou, Fuhao Cao, Wanhua Jia, Xiaoyun Li, Kai Jiang, Chuntao Wuhan Digital Engineering Research Institute Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Natural and Computational Science Massey University Wellington New Zealand School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Big Data Foshan University Foshan China

Remote-sensing object detection is a challenging task due to the difficulties of separating the objects with arbitrary direction from complex backgrounds. Though substantial progress has been made, there still exist challenges for object detection under the scenario of small scale, large aspect ratio, and dense distribution. Besides, the current mainstream approach falls under anchor-based multi-stage method, which has a serious shortcoming of slower inference speed. To conquer the aforementioned issues, this paper used RoPoints (points in rotation objects), a new better representation of objects as a set of sample points to perform object localization and classification. Then, we propose an anchor-free refined rotation detector:ROPDet based on RoPoints for more accurate and faster object detection. In our method, there is no need to predefine a large number anchors with different shapes. We only need to learn RoPoints for each object followed by converting to the corresponding bounding box, which greatly accelerates the inference process. Extensive experiments on two public remote-sensing datasets DOTA and HRSC-2016 demonstrate the competitive ability in terms of accuracy and inference speed. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Object detection

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Intra-datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Damola Gideon Akinola Emmanuel Adetiba Abdultaofeek Abayomi Uche Nnaji Surendra Thakur Sibusiso Moyo Department of Electrical and Information Engineering Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Durban South Africa Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria KZN e-Skills CoLab Durban University of Technology Durban South Africa School for Data Science and Computational Thinking and Department of Mathematical Sciences Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.

关键词： computers Cloud computing Organizations Load management Resource management Time factors Task analysis

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Foundations of symbolic languages for model interpretability 21

Foundations of symbolic languages for model interpretability

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Marcelo Arenas Daniel Baez Pablo Barceló Jorge Pérez Bernardo Subercaseaux Department of Computer Science PUC-Chile and Millennium Institute for Foundational Research on Data Chile Department of Computer Science Universidad de Chile Institute for Mathematical and Computational Engineering PUC-Chile and Millennium Institute for Foundational Research on Data Chile Department of Computer Science Universidad de Chile and Millennium Institute for Foundational Research on Data Chile Millennium Institute for Foundational Research on Data Chile and Carnegie Mellon University

ISBN: (纸本)9781713845393

Several queries and scores have been proposed to explain individual predictions made by ML models. Examples include queries based on "anchors", which are parts of an instance that are sufficient to justify its classification, and "feature-perturbation" scores such as SHAP. Given the need for flexible, reliable, and easy-to-apply interpretability methods for ML models, we foresee the need for developing declarative languages to naturally specify different explainability queries. We do this in a principled way by rooting such a language in a logic called FOIL, that allows for expressing many simple but important explainability queries, and might serve as a core for more expressive interpretability languages. We study the computational complexity of FOIL queries over classes of ML models often deemed to be easily interpretable: decision trees and more general decision diagrams. Since the number of possible inputs for an ML model is exponential in its dimension, tractability of the FOIL evaluation problem is delicate, but can be achieved by either restricting the structure of the models, or the fragment of FOIL being evaluated. We also present a prototype implementation of FOIL wrapped in a high-level declarative language, and perform experiments showing that such a language can be used in practice.

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A Survey on Distributed Evolutionary Computation

arXiv

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arXiv 2023年

作者： Chen, Wei-Neng Wei, Feng-Feng Zhao, Tian-Fang Tan, Kay Chen Zhang, Jun The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Guangdong-Hong Kong Joint Innovative Platform of Big Data and Computational Intelligence South China University of Technology Guangzhou510006 China The School of Journalism and Communication Jinan University Guangzhou510006 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Electrical and Electronic Engineering Hanyang University Ansan15588 Korea Republic of

The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can significantly improve the efficiency and scalability of EC. On the other hand, data are collected and processed in a distributed manner, which brings a novel development direction and new challenges to EC. In this paper, we intend to give a systematic review on distributed EC (DEC). First, a new taxonomy for DEC is proposed from top design mechanism to bottom implementation mechanism. Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment. Second, we clarify two major purposes of DEC, i.e., improving efficiency through parallel processing for centralized optimization and cooperating distributed individuals/sub-populations with partial information to perform distributed optimization. Third, noting that the latter purpose of DEC is an emerging and attractive trend for EC with the booming of spatially distributed paradigms, this paper gives a systematic definition of the distributed optimization and classifies it into dimension distributed-, data distributed-, and objective distributed-optimization problems. Formal formulations for these problems are provided and various DEC studies on these problems are reviewed. We also discuss challenges and potential research directions, aiming to enlighten the design of DEC and pave the way for future developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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